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Abstract
Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug–target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models.
Highlights
Drug discovery (DD) is a time-consuming and expensive process in which, despite the recent technological advancements and the increasing investments, most of the compounds examined fail during clinical trials or due to toxic and adverse side effects [1–3]
In the first months of 2020, approximately 70 existing FDA-approved drugs were under investigation to see if they could be re-purposed to treat COVID-19 [7]; disease-modifying pharmacotherapies are being repurposed to treat Parkinson’s Disease [10]; the repurposing in cardiovascular diseases of drugs approved and marketed for other pathologies [11]; genome-wide association studies (GWASs) that involve the use of human genetic data to link genes to specific diseases have
The Accuracy score (ACC) was employed to understand how good was the model at considering a binary classification problem
Summary
Drug discovery (DD) is a time-consuming and expensive process in which, despite the recent technological advancements and the increasing investments, most of the compounds examined fail during clinical trials or due to toxic and adverse side effects [1–3]. The industry aims to find new uses for already approved drugs, avoiding the expensive and lengthy process of drug development [5,6]. This strategy is known as: (i) drug repositioning, which usually refers to the studies that reinvestigate existing drugs that failed approval for new therapeutic indications [7], and (ii) drug repurposing, which suggests the application of already approved drugs and compounds to treat a different disease [8,9]. Pharmaceutics 2021, 14, 625 already resulted in candidate targets for drug discovery and repurposing [12]; and the drug.
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